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CityDPC-MCP

CityDPC-MCP

by HamerCode
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_all_buildings

Retrieve all buildings and their attributes from a CityJSON or CityGML dataset to inspect and analyze building data.

Instructions

Gibt Informationen zu allen Gebäuden im Dataset zurück.

Returns: list[dict]: Liste mit Dictionaries aller Gebäude und deren Attributen

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_all_buildings"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It says what is returned but omits whether the operation is read-only, requires a loaded dataset, may be expensive for large collections, or has any side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose. The second sentence restates return type, which is redundant given the output schema exists, but it is not excessive.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a no-argument read tool with an output schema, the return values are covered elsewhere. However, the description does not state that a dataset must be loaded first, which is important context in a dataset-oriented toolset.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the baseline is 4. The description adds no parameter information because there is none to add.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear verb+resource ('Gibt Informationen zu allen Gebäuden im Dataset zurück') and scope ('allen Gebäuden'). The scope distinguishes it implicitly from siblings like get_building_by_id or number_of_buildings, but it does not name an alternative explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance or alternatives are given. The agent must infer from the name alone that this is for retrieving all buildings rather than a count, an ID list, or a single building.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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